Personalized active learning for collaborative filtering

By Abhay Harpale, Yiming Yang. 2008

135 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

Learning Algorithms for Active Learning (2017, methodology): “The cold-start problem is ubiquitous in recommendation systems (Aggarwal, 2016; Lika et al., 2014; Harpale & Yang, 2008; Sun et al., 2013b; Elahi et al., 2016).”
Cold-start Active Learning with Robust Ordinal Matrix Factorization (2014, methodology): “In this case we draw samples from Q ( v j ) for a new item with index j , where v j is the item’s latent vector.”
Functional matrix factorizations for cold-start recommendation (2011, background): “We also want to mention active learning for collaborative ﬁltering [4,11,13,17].”
Learning multiple-question decision trees for cold-start recommendation (2013, methodology): “For a better cold-start recommender system, we advocate designs focusing on asking multiple questions at each trial.”
A survey of active learning in collaborative filtering recommender systems (2016, methodology): “The majority of the works in this research area [59,20,68, 83,60,84,61,93,63,94,69,95] have focused on offline evaluations and only a few have been conducted online [81,77,20,96,68].”
Music recommendation by unified hypergraph: combining social media information and music content (2010, unknown): “Traditional recommendation algorithms, such as Collaborative Filtering (CF ) [29, 16, 21], only consider the user-item rating matrix and fail to take advantage of other kinds of social media information.”
Short-Term Satisfaction and Long-Term Coverage: Understanding How Users Tolerate Algorithmic Exploration (2018, background): “This motivates the need for research on more constrained presentation strategies, e.g., strategies that expose people only to items they are likely to know [14, 32].”
Interactive collaborative filtering (2013, background): “Active learning is also deployed in order to identify the most informative set of training examples (items) with respect to some selection criterion, such as the expected information gain [18, 21], with the target of minimizing the number of interactions.”